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Lesson 11: Pydantic AI - Type-Safe Agents

Topics Covered
  • The Case for Type Safety: Why types matter in production agents.
  • Pydantic AI vs LangChain: Different philosophies, different strengths.
  • Defining Agents: Typed system prompts, dependencies, and outputs.
  • Structured Tools: Type-safe tool definitions with automatic validation.
  • Result Validation: Ensuring agent outputs match your schema.
  • Production Patterns: Error handling, retries, and testing.

LangChain prioritizes flexibility and ecosystem breadth. Pydantic AI takes a different approach: type safety as the foundation for reliability. When your agent's output feeds into downstream systems, you need guarantees—not just hopes—that the data matches your expectations. In this lesson, you'll build agents where inputs, outputs, and tool calls are all validated at runtime.

Synopsis​

1. Why Type Safety for Agents​

  • The problem: LLM outputs are unpredictable strings
  • Runtime failures when outputs don't match expectations
  • The Pydantic philosophy: validate everything at the boundary
  • Type hints as documentation and contracts
  • Catching errors early vs debugging production failures

2. Pydantic AI vs LangChain​

  • LangChain: flexibility-first, massive ecosystem, loose typing
  • Pydantic AI: types-first, focused scope, strict validation
  • When to choose each framework
  • Can they work together? (Using Pydantic models in LangChain)

3. Your First Pydantic AI Agent​

  • Installing Pydantic AI
  • The Agent class: system prompt, model, result type
  • Defining typed outputs with Pydantic models
  • Running agents with agent.run()
  • Inspecting results and validation errors

4. Dependencies: Injecting Context​

  • What are dependencies (database connections, API clients, user context)
  • Defining dependency types
  • Injecting dependencies at runtime
  • Testing agents with mock dependencies
  • Dependency lifecycle management

5. Structured Tools with Type Safety​

  • Defining tools as typed functions
  • Automatic schema generation from type hints
  • Tool return types and validation
  • Handling tool errors gracefully
  • Composing multiple tools

6. Result Validation and Retries​

  • What happens when the LLM returns invalid output
  • Automatic retry with validation feedback
  • Configuring retry behavior
  • Custom validators for complex rules
  • Fallback strategies when validation fails

7. Streaming and Async Patterns​

  • Streaming partial results with types
  • Async agent execution
  • Parallel tool calls
  • Timeout handling
  • Cancellation patterns

8. Testing Pydantic AI Agents​

  • Unit testing with mock models
  • Integration testing strategies
  • Snapshot testing for agent outputs
  • Testing tool execution paths
  • CI/CD considerations for agent tests

9. Pydantic AI in Production​

  • Structured logging with typed outputs
  • Metrics and monitoring
  • Cost tracking
  • Error reporting and alerting
  • Versioning agent configurations

Additional Resources​